Mining Valuable Fuzzy Patterns via the RFM Model

This paper aims to propose an effective algorithm to discover valuable patterns by applying the fuzzy method to the RFM model. RFM analysis is a common method in customer relationship management, through which we can identify valuable customer groups. By combining RFM analysis with frequent pattern mining, valuable RFM - patterns can be found from the RFM-pattern-tree, such as the RFMP-growth algorithm. Aiming to mine patterns that have quantitative relationships among items, we introduce the fuzzy method in the RFM model, and we present a fuzzy - Rfu - tree algorithm in which a new pruning strategy is proposed to prune candidate patterns. Experiments show the effectiveness of the new algorithm. The new algorithm guarantees a high overlap degree with the RFM-patterns gen-erated by RFMP-growth, with more valuable information (with additional fuzzy level) in the mined patterns.